ProbAI: A Hub for the Mathematical and Computational Foundations of Probabilistic AI

概率人工智能涉及在人工智能方法中嵌入概率模型、概率推理和不确定性度量。ProbAI中心将开发一个世界领先的、多样化的、全英国范围的概率人工智能研究项目，该项目将开发下一代数学严谨、可扩展和不确定性感知的人工智能算法。它将对人工智能的许多方面产生深远的影响，包括：(1)人工智能系统的突然和快速增长为企业、政府和人工智能工具的创造者带来了新的动力，以理解和传达其系统中固有的不确定性。人工智能的概率方法提供了一个框架来表示和操纵模型和预测的不确定性，并且已经在科学数据分析、机器人和认知科学中发挥了核心作用。这种发展所产生的后续影响可能是广泛而实质性的：利用概率方法进行有效的资源分配（医疗保健）、确定行动的优先次序（基础设施规划）、模式识别（网络安全）以及制定强有力的战略来降低风险（金融）。(2)通过研究AI模型和算法在不同渐近场景下的极限行为（通常是概率性的），有可能获得对AI模型和算法的重要理论见解。这样的结果可以帮助理解人工智能方法工作的原因，以及如何最好地选择合适的架构——有可能大幅降低人工智能的计算成本和碳足迹。(3)生成模型的最新突破是基于对随机过程的模拟。我们可以利用这些理念去开发更有效且可扩展的概率AI方法；同时改进和扩展当前的生成模型。后者可能导致更多的计算效率和鲁棒的方法，生成模型，使用不同的随机过程，适用于不同类型的数据，或新的方法，可以给一个生成模型的输出一定程度的确定性。(4)人工智能模型越来越多地被用作仿真器。例如，将一个深度神经网络拟合到一个复杂的天气计算机模型的实现中，可以产生更有效的天气预报方法。然而，在大多数应用程序中，为了可靠地使用这些方法，需要模拟器报告不确定性的度量——这样用户就可以知道输出何时可以信任。此外，基于贝叶斯更新的最新泛化，提供了将已知物理约束和其他结构纳入这些神经网络模拟器的新方法，从而产生更鲁棒的方法，这些方法在训练采样器之外泛化得更好，参数更少，更容易拟合。开发这些新的、实用的、通用的概率人工智能方法需要克服大量的挑战，而这些挑战的核心是数学。该中心将统一一个对概率人工智能感兴趣的零散社区，并汇集英国应用数学、计算机科学、概率和统计学领域的研究人员。该中心将广泛促进概率人工智能领域的发展，鼓励和促进人工智能领域的跨学科数学研究，并在其生命周期内为来自英国各地的研究人员提供资金支持。ProbAI将利用并受益于在数学和计算机科学的不同领域与概率人工智能相关的领域中建立的世界领先的实力，目的是使英国成为概率人工智能的世界领导者。

Probabilistic AI involves the embedding of probability models, probabilistic reasoning and measures of uncertainty within AI methods. The ProbAI hub will develop a world leading, diverse and UK-wide research programme in probabilistic AI, that will develop the next generation of mathematically-rigorous, scalable and uncertainty-aware AI algorithms. It will have far-reaching impact across many aspects of AI, including:(1) The sudden and rapid growth of AI systems has led to a new impetus for businesses, governments and creators of AI tools to understand and convey the inherent uncertainties in their systems. A probabilistic approach to AI provides a framework to represent and manipulate uncertainty about models and predictions and already plays a central role in scientific data analysis, robotics and cognitive science. The consequential impact arising from from such developments has the potential to be wide-ranging and substantial: from utilising a probabilistic approach for effective resource allocation (healthcare), prioritisation of actions (infrastructure planning), pattern recognition (cyber security) and the development of robust strategies to mitigate risks (finance).(2) It is possible to gain important theoretical insights into AI models and algorithms through studying their, often probabilistic, limiting behaviour in different asymptotic scenarios. Such results can help with understanding why AI methods work, and how best to choose appropriate architectures - with the potential to substantially reduce the computational cost and carbon footprint of AI.(3) Recent breakthroughs in generative models are based on simulating stochastic processes. There is huge potential to both use these ideas to help develop efficient and scalable probabilistic AI methods more generally; and also to improve and extend current generative models. The latter may lead to more computationally efficient and robust methods, to generative models that use different stochastic processes and are suitable for different types of data, or to novel approaches that can give a level of certainty to the output of a generative model. (4) Models from AI are increasingly being used as emulators. For example, fitting a deep neural network to realisations of a complex computer model for the weather, can lead to more efficient approaches to forecasting the weather. However, in most applications for such methods to be used reliably requires that the emulators report a measure of uncertainty -- so the user can know when the output can be trusted. Also, building on recent generalisations of Bayes updates gives new approaches to incorporate known physical constraints and other structure into these neural network emulators, leading to more robust methods that generalise better outside the training sampler and that have fewer parameters and are easier to fit.Developing these new, practical, general-purpose probabilistic AI methods requires overcoming substantial challenges, and at their heart many of these challenges are mathematical. The hub will unify a fragmented community with interests in Probabilistic AI and bring together UK researchers across the breadth of Applied Mathematics, Computer Science, Probability and Statistics. The hub will promote the area of probabilistic AI widely, encouraging and facilitating cross-disciplinary mathematics research in AI, and has substantial flexibility to fund the involvement of researchers from across the breadth of the UK during its lifetime.ProbAI will draw on and benefit from the well-established world-leading strength in areas relevant to probabilistic AI across different areas of Mathematics and Computer Science, with the aim of making the UK the world-leader in probabilistic AI.